Optimizing Photovoltaic Generation Placement and Sizing Using Evolutionary Strategies Under Spatial Constraints

Carlos Henrique Silva, Saymon Fonseca Santos Mendes, Lina P. Garcés Negrete (), Jesús M. López-Lezama and Nicolás Muñoz-Galeano
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Carlos Henrique Silva: Electrical, Mechanical and Computer Engineering School, Federal University of Goiás (UFG), Av. Universitária No. 1488, Goiânia 74605-010, Brazil
Saymon Fonseca Santos Mendes: Electrical, Mechanical and Computer Engineering School, Federal University of Goiás (UFG), Av. Universitária No. 1488, Goiânia 74605-010, Brazil
Lina P. Garcés Negrete: Electrical, Mechanical and Computer Engineering School, Federal University of Goiás (UFG), Av. Universitária No. 1488, Goiânia 74605-010, Brazil
Jesús M. López-Lezama: Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Calle 67 No. 53–108, Medellín 050010, Colombia
Nicolás Muñoz-Galeano: Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Calle 67 No. 53–108, Medellín 050010, Colombia

Energies, 2025, vol. 18, issue 8, 1-34

Abstract: This study presents a methodology for optimizing the placement and sizing of photovoltaic generation in power distribution networks. In addition to technical and budgetary constraints, the proposed approach incorporates georeferenced spatial restrictions to determine the optimal location and capacity of the generation units. These spatial constraints are not commonly considered in similar studies, which make them the main contribution in the proposed methodology. The proposed approach is divided into three stages and utilizes simulations in OpenDSS and QGIS, which employ optimization strategies such as the Hybrid Evolutionary Strategy and the Hybrid Genetic Algorithm. The methodology was evaluated on the IEEE 34-bus system and a real feeder. The results demonstrate the effectiveness of the proposed approach, which achieves significant reductions in system losses—14.48% for the IEEE 34-bus system and 14.08% for the real feeder—while also improving voltage profiles. These findings validate its applicability in the efficient and sustainable planning of power distribution systems.

Keywords: electric power distribution systems; evolutionary strategies; genetic algorithms; OpenDSS; photovoltaic generation; QGIS; spatial constraints (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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